Reducing Critical Service Loss through Coordinated Resilience - PDF document

Reducing Critical Service Loss through Coordinated Resilience Planning Julian Watts¹ 1 Gutteridge Haskins & Davey Limited (GHD), Horton House, Exchange Flags, Liverpool, L2 3PF, United Kingdom Tel. (+44) (0) 755 445 1698 Fax (+44) (0) 151 244 5041 Email, julian.watts@ghd.com Web www.ghd.com Summary: In the modern economy, the drive to manage assets efficiently and effectively is stronger than ever. Recent catastrophic events affecting people and assets have resulted in a surge of Business Continuity Planning requirements on the boardroom agenda. This paper outlines a process to identify potentially vulnerable critical infrastructure during emergency events that would require detailed response plans. It summarises observations from traditional disaster planning methods and compares outputs from vulnerability analysis and Geographical Information Systems modeling undertaken for a Local Authority in New Zealand. These outputs illustrate the benefits of coordinated analysis across local roads, water services and wastewater services. KEYWORDS: Critical Vulnerability, Resilience Planning, Business Continuity, Cross-Network Infrastructure, Rena 1. Introduction Requirements to undertake risk and analysis for lifelines and critical infrastructure protection have been in place and publicised for many years as seen in Crimp (2008). Likewise, recognised concepts of Plan-Do-Check-Act (PDCA) have been embedded in asset management standards like BSI PAS-55 (2008) and Optimised Renewal Decision Making (ODRM) is part of our everyday manuals such as the International Infrastructure Management Manual (2011). Why then, is resilience planning focussed on protection of a single asset class and so easily overlooked for a set of crossing but unconnected networks? Possibly because ODRM is only applied on assets that custodians have direct influence on. Possibly because there are no agreed guidelines that outline the scope of impact analysis that should be undertaken. Several lifeline studies have been completed in the Bay of Plenty region in New Zealand, however these generally focus on one location, asset or scenario at a time. This paper does not seek to recommend modeling of every scenario, however it demonstrates how a broader planning approach can identify mitigation options for unaccounted for eventualities, such as the Rena (2011) oil spill that was not a considered scenario however benefited from an accelerated response. 1.1 The Compounding Impact Recent catastrophic events, exhibit compounding effects of disasters that impact simultaneously on multiple services. These include mobility, electricity, potable water, haulage of goods and availability of community centres. These compounding effects can have devastating impacts on the economy, driving communities and businesses out of city centres, causing lasting changes to regions. A recent report on climate change by the Commonwealth of Australia (2009) stated "even if the cost of protection was AUD$10 billion for Melbourne alone, it would still be a lower cost alternative to losing low-lying infrastructure, building assets and the cost of disruption to the local economy and society". Business Continuity Planning (BCP) assists in dealing with the ensuing aftermath from events like these however they do not address the ability to build resilience into the infrastructure to standard procedures. 1.2 Aspects to Combine A paper on lifecycle analysis of existing infrastructure by Magnuson and Amador (2010) looks at

coordinating renewals and reducing the long term cost of replacing essential services. This methodology aligns with good practice asset management however only considers time of optimised renewal, not optimising the replaced asset. Another promising paper by Bruneau et al. (2003) looks at performance indicators to identify resilient communities and has a conceptual framework that could be applied in a regulatory context. This model would work best for measuring resilience using a coordinated approach. It does not utilise the feedback cycle ORDM to build resilience into core infrastructure. The International Standard on Business Continuity ISO/PAS-22399 (2007) covers multidisciplinary resilience planning. However this is only for one organisation and it is not designed to set procedures for post disaster recovery. 2. Case Study: Cross Asset Vulnerability Analysis Tauranga City Council (TCC) engaged GHD New Zealand Ltd. to lead the development of a vulnerability assessment for City Transportation and City Waters to be delivered to the Bay of Plenty Lifelines Advisory Group (BOPLAG), a member of the New Zealand Engineering Lifeline Groups. During the project it was identified that different results were captured if analysis was undertaken on a single network than when undertaken in a combined spatial environment. Therefore the results of the individual studies have been combined to illustrate a more complete output. The purpose was to identify potential high risk areas within communities that are susceptible to damage during natural disaster events or service outages, in order to capture region-wide mitigation planning. The objective was to review asset criticality in the transportation, water and wastewater networks, assess their vulnerability under 27 different severe climate scenarios and deliver optimum outputs to the BOPLAG as set out in Table 1. Table 1. Natural Event Scenarios Code Scenario Event Flash Flood FL Flooding Inundation Landslide Chemical Action GE Geothermal Ground Settlement Ground Shaking Sewerage IL Infrastructure Local Authority Stormwater Water Electricity IP Infrastructure Private Gas Telecoms Fault Displacement SE Seismic Ground Settlement Ground Shaking Landslide Liquefaction Coastal Erosion SS Storm Surge Inundation Coastal Erosion TS Tsunami Inundation Velocity Damage Ash Fall VO Volcanic Lahar Water

Code Scenario Event Fire WF Wind Fire Wind The locations of critical assets across different networks were modelled in a Geographical Information System (GIS), ESRI ArcGIS. This assisted in identifying areas of high risk with a dependency on other infrastructure networks. Critical assets were overlaid with soil maps to identify drainage issues, contour maps to identify tsunami impacts and liquefaction maps to identify earthquake-prone areas. Under each scenario, assets identified as ‘vulnerable’ had potential risk mitigation techniques documented so that these actions could be combined where vulnerable hot- spots emerged. 2.1 Process Undertaken Critical assets were defined through meetings with Transportation, Water and Wastewater network managers and asset managers. The process used to identify the vulnerable assets was explained and is illustrated in Figure 1. Figure 1. Process to Assess Vulnerability Under each scenario, critical assets were scored on three dimensions; importance to the network, population affected if service was disrupted and likelihood of being affected by the scenario. The impact or consequence was then scored at four stages during the event. This resulted in an understanding of when an asset becomes vulnerable and aids prioritisation. This process allowed all critical assets to be ranked by vulnerability across the entire city.

2.2 Risk Score Definitions Asset classes were scored across three dimensions using definitions provided by BOPLAG (Table 2): Table 2. Risk Definitions Ranking Importance (population or Vulnerability Impact people served) (likelihood) (consequence) 5 Extremely important Almost certain Catastrophic 4 Very important Likely Major 3 Important Possible Moderate 2 Some importance Unlikely Minor 1 Not important Rare Insignificant 2.3 Risk Time Factor Each event’s impact is scored with a time factor to indicate when the most severe damage would occur. The descriptions below are used to define each time period that should be scored. Table 3. Chronological Assessment of Impact Codes Chronological Code Description DE During Event IA Immediately After PF Period Following RN Return to Normal RT Return Time (Weeks) 2.4 Data Sources Utilised to Define Asset Classes The Transportation (RAMM), Water and Wastewater (Hansen) Asset Management Systems (AMS), and GIS databases were reviewed to collate the set of asset classes. The soil maps were sourced from the Western Bay of Plenty Lifelines Study : Microzoning for Earthquake Hazards (2002) report. The asset classes identified are as follows: Table 4. Asset Classes Reviewed Network Type Transportation Bridges Retaining Walls Embankments Arterial Pavements Wastewater Pump Stations Reticulation - Gravity Rising Mains Treatment Plant Water Supply Booster Pump Stations Intakes Interchange Pipe Bridges Processing Plants Reservoirs Reticulation - Distribution Reticulation - Raw